Functional magnetic resonance imaging (fMRI) has become an important neuroscientific tool for probing neural mechanisms in the human brain. Typical fMRI experiments have focused on the acquisition of T2*-sensitive MR images during periods of increased oxygen consumption (owing to neuronal response to externally controlled experimental conditions) and contrast the measured image intensities with recordings obtained at ‘rest’. Critically, some important quantitative concepts in fMRI analysis, such as the calculation of per cent signal change or the interpretation of de-activation, implicitly hinge on a suitable definition of this baseline/rest signal. The baseline ‘resting-state’ of the brain itself, however, is a somewhat ill-defined and poorly understood concept.
Of particular interest in this context are certain low-frequency fluctuations of the measured cerebral haemodynamics (around 0.01–0.1
Hz) which exhibit complex spatial structure reminiscent of fMRI ‘activation maps’ and which can be identified in fMRI data taken both under rest condition and under external stimulation. Recently, some attention has been focused on the characterization of these maps and the identification of possible origins of slow variations in the measured blood-oxygen level dependent (BOLD) signal. Various researchers have suggested that these signal variations, temporally correlated across the brain, are of neuronal origin and correspond to functional resting-state networks (RSNs) which jointly characterize the neuronal baseline activity of the human brain in the absence of deliberate and/or externally stimulated neuronal activity, and may reflect functionally distinct networks.
Biswal et al. (1995)
first demonstrated the feasibility of using fMRI to detect such spatially distributed networks within primary motor cortex during resting-state by calculating temporal correlations across the brain with the time-course from a seed voxel whose spatial location was chosen from a prior finger-tapping study. The temporal signal from a seed voxel in the motor cortex was correlated with other motor cortex voxels and uncorrelated with other voxels, with major frequency peaks in the resting correlations at around 0.02
Hz. Lowe et al. (1998)
found similar results using both single-slice dates with low time of repetition (TR) of 130
ms and whole-head volumes with longer TR (2000
ms), while Xiong et al. (1999)
describe functional connectivity maps that cover additional non-motor areas. Based also on findings from positron emission tomography (PET) studies, the existence of a default mode brain network
involving several regions including the posterior cingulate cortex has been proposed (Shulman et al. 1997
; Mazoyer et al. 2001
; Raichle et al. 2001
). Using simultaneously acquired electroencephalogram (EEG) and fMRI data under rest, Goldman et al. (2002)
have shown that the variation in alpha rhythm in EEG (8–12
Hz) is correlated with the fMRI measurements. In particular, the authors report that increased alpha power was correlated with decreased BOLD signal in multiple regions of occipital, superior temporal, inferior frontal and cingulate cortex, and with increased signal in the thalamus and insula. These results have important implications for interpretation of RSNs, as they suggest a neuronal cause for these fluctuations.
Alternatively, it has been argued that these effects simply reflect vascular processes unrelated to neuronal function, which would make RSNs of less interest to neuroscience (although still of potential clinical interest). Physiological noise in the resting brain and its echo-time (TE) and field-strength dependencies were investigated by Kru¨ger & Glover (2001)
, who showed that physiological noise demonstrates a field-strength dependency, exceeds the thermal as well as scanner noise at 3T and is increased in grey matter (e.g. Woolrich et al. 2001
). Various researchers have investigated the relation between low-frequency fluctuations in the measured BOLD signal and other physiological observations. Obrig et al. (2000)
reviewed and studied low-frequency variations in oxygenation, cerebral blood flow and metabolism, and report significant correlations with similar fluctuations observed by near infrared spectroscopy (NIRS). More recently, Wise et al. (2004)
have investigated the influence of arterial carbon dioxide fluctuations by using the endtidal level of exhaled carbon dioxide as covariate of interest in a general linear model (GLM) analysis. The most significant changes were concentrated in the occipital, parietal and temporal lobes, as well as in the cingulate cortex, and suggest that vascular processes (unrelated to neuronal function) play a significant role in the generation of such resting-state patterns.
Estimating the temporal and spatial characteristics of these low-frequency fluctuations from fMRI data presents a formidable challenge to analytical techniques. In the majority of existing studies, resting patterns are inferred by a correlation analysis of the voxel-wise fMRI recordings against a reference time-course obtained from secondary recordings (e.g. from EEG, NIRS or physiologic measurements such as the carbon dioxide concentration), or simply by regressing against a single voxel's time-course from resting data, which is believed to be of functional relevance (seed-voxel-based correlation analysis). These techniques fundamentally test very specific hypotheses about the temporal structure of these effects. Recently, however, independent component analysis (ICA) has successfully been applied to the estimation of certain low-frequency patterns (Goldman & Cohen 2003
; Kiviniemi et al. 2003
; Greicius et al. 2004
). An important benefit of such exploratory techniques over more hypothesis-based techniques is the ability to identify various types of signal fluctuations by virtue of their spatial and/or temporal characteristics without the need to specify an explicit temporal model. Such flexibility in data modelling is essential in cases where the effects of interest are not well understood and cannot be predicted accurately.
This paper is organized as follows: in §2
we review a probabilistic approach to independent component analysis (PICA) specifically optimized for the analysis of fMRI data (Beckmann & Smith 2004
). Section 3
discusses the constraints of this exploratory data analysis technique when used for the identification of large-scale noise fluctuations. In particular, we demonstrate that optimization for maximally independent spatial sources does not imply an inability to estimate largely overlapping spatial maps. We demonstrate the ability of PICA to extract resting fluctuations and apply the technique to fMRI resting data in order to test a set of important hypotheses about the structure of resting-state connectivity in the human brain. In particular, we will investigate (i) if and how estimated source processes are driven by less-interesting physiological effects such as the cardiac or respiratory cycle, (ii) the spatial characteristics of estimated maps in terms of locality within grey matter and (iii) the consistency of maps obtained from multiple subjects.